With the rapid development of e-economy, ordering via online food delivery platforms has become prevalent in recent years. Nevertheless, the platforms are facing lots of challenges such as time-limitation and uncertainty. This paper addresses a complex stochastic online route-planning problem (SORPP) which is mathematically formulated as a two-stage stochastic programming model. To meet the immediacy requirement of online fashion, an end-to-end deep learning model is designed which is composed of an encoder and a decoder. To embed different problem-specific features, different network layers are adopted in the encoder; to extract the implicit relationship, the probability mass functions of stochastic food preparation time is processed by a convolution neural network layer; to provide global information, the location map and rider features are handled by the factorization-machine (FM) and deep FM layers, respectively; to screen out valuable information, the order features are embedded by attention layers. In the decoder, the permutation sequence is predicted by long-short term memory cells with attention and masking mechanism. To learn the policy for finding optimal permutation under complex constraints of the SORPP, the model is trained in a supervised learning way with the labels obtained by iterated greedy search algorithm. Extensive experiments are conducted based on real-world data sets. The comparative results show that the proposed model is more efficient than meta-heuristics and is able to yield higher quality solutions than heuristics. This work provides an intelligent optimization technique for complex online food delivery system.

随着电子经济的迅猛发展，近年来，通过在线食品配送平台进行订购变得越来越普遍。尽管如此，平台仍面临许多挑战，例如时限和不确定性。本文解决了一个复杂的随机在线路线规划问题（SORPP），该问题在数学上被表述为两阶段随机规划模型。为了满足在线时尚的即时性需求，设计了一个由编码器和解码器组成的端到端深度学习模型。为了嵌入不同的问题特定功能，编码器采用了不同的网络层。为了提取隐式关系，通过卷积神经网络层处理随机食物制备时间的概率质量函数。提供全球信息，位置地图和骑乘者特征分别由分解机（FM）和深层FM层处理；为了筛选出有价值的信息，订单功能嵌入在关注层中。在解码器中，置换序列由具有注意和掩蔽机制的长期短期存储单元预测。为了学习在SORPP的复杂约束下找到最佳排列的策略，该模型以监督学习的方式训练，并带有通过迭代贪婪搜索算法获得的标签。基于实际数据集进行了广泛的实验。比较结果表明，所提出的模型比元启发式算法更有效，并且能够提供比启发式算法更高质量的解决方案。这项工作为复杂的在线食品配送系统提供了一种智能优化技术。